59 research outputs found
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Investigating the overhead of the REST protocol to reveal the potential for using cloud services for HPC storage
With the significant advances in Cloud Computing, it is inevitable to explore the usage of Cloud technology in HPC workflows. While many Cloud vendors offer to move complete HPC workloads into the Cloud, this is limited by the massive demand of computing power alongside storage resources typically required by I/O intensive HPC applications. It is widely believed that HPC hardware and software protocols like MPI yield superior performance and lower resource consumption compared to the HTTP transfer protocol used by RESTful Web Services that are prominent in Cloud execution and Cloud storage. With the advent of enhanced versions of HTTP, it is time to reevaluate the effective usage of cloud-based storage in HPC and their ability to cope with various types of data-intensive workloads. In this paper, we investigate the overhead of the REST protocol via HTTP compared to the HPC-native communication protocol MPI when storing and retrieving objects. Albeit we compare the MPI for a communication use case, we can still evaluate the impact of data communication and, therewith, the efficiency of data transfer for data access patterns. We accomplish this by modeling the impact of data transfer using measurable performance metrics. Hence, our contribution is the creation of a performance model based on hardware counters that provide an analytical representation of data transfer over current and future protocols. We validate this model by comparing the results obtained for REST and MPI on two different cluster systems, one equipped with Infiniband and one with Gigabit Ethernet. The evaluation shows that REST can be a viable, performant, and resource-efficient solution, in particular for accessing large files
Data-driven model of the hippocampus using the HBP Brain Simulation Platform
The hippocampus is one of four brain regions being modeled in the ramp-up phase of the Human Brain Project (HBP), testing and guiding the development of the HBP Brain Simulation Platform (BSP) to be released in March 2016. Using preliminary versions of BSP applications developed at the Blue Brain Project, a first draft data-driven model of hippocampus was assembled, integrating data available from HBP and community sources. In brief, the building process started by populating the hippocampal volume, defined by the Allen Brain Atlas, with a series of reconstructions of well-characterized cell types according to experimentally observed densities and proportions. A connectome was generated as previously described [1], constrained by biological values for bouton density and synapses per connection. Single cell electrical models and synapse physiology were constrained by electrophysiological recordings and publicly available data. Further datasets not used as input during model building were used to validate the model. This first draft of the circuit model and the pipeline to build it are to be released with the HBP-BSP in March 2016, and they will be periodically updated. The model represents a resource for the community to integrate data, perform in silico experiments, and test hypotheses. Establishing a community process for the continued refinement of the model is planned for the next phase of the HBP.
[1] Reimann, M. et al. An algorithm to predict the connectome of neural microcircuits. Front. Comput. Neurosci. (2015). http://dx.doi.org/10.3389/fncom.2015.0012
Effect of the COVID-19 pandemic on surgery for indeterminate thyroid nodules (THYCOVID): a retrospective, international, multicentre, cross-sectional study
Background Since its outbreak in early 2020, the COVID-19 pandemic has diverted resources from non-urgent and elective procedures, leading to diagnosis and treatment delays, with an increased number of neoplasms at advanced stages worldwide. The aims of this study were to quantify the reduction in surgical activity for indeterminate thyroid nodules during the COVID-19 pandemic; and to evaluate whether delays in surgery led to an increased occurrence of aggressive tumours.Methods In this retrospective, international, cross-sectional study, centres were invited to participate in June 22, 2022; each centre joining the study was asked to provide data from medical records on all surgical thyroidectomies consecutively performed from Jan 1, 2019, to Dec 31, 2021. Patients with indeterminate thyroid nodules were divided into three groups according to when they underwent surgery: from Jan 1, 2019, to Feb 29, 2020 (global prepandemic phase), from March 1, 2020, to May 31, 2021 (pandemic escalation phase), and from June 1 to Dec 31, 2021 (pandemic decrease phase). The main outcomes were, for each phase, the number of surgeries for indeterminate thyroid nodules, and in patients with a postoperative diagnosis of thyroid cancers, the occurrence of tumours larger than 10 mm, extrathyroidal extension, lymph node metastases, vascular invasion, distant metastases, and tumours at high risk of structural disease recurrence. Univariate analysis was used to compare the probability of aggressive thyroid features between the first and third study phases. The study was registered on ClinicalTrials.gov, NCT05178186.Findings Data from 157 centres (n=49 countries) on 87 467 patients who underwent surgery for benign and malignant thyroid disease were collected, of whom 22 974 patients (18 052 [78 center dot 6%] female patients and 4922 [21 center dot 4%] male patients) received surgery for indeterminate thyroid nodules. We observed a significant reduction in surgery for indeterminate thyroid nodules during the pandemic escalation phase (median monthly surgeries per centre, 1 center dot 4 [IQR 0 center dot 6-3 center dot 4]) compared with the prepandemic phase (2 center dot 0 [0 center dot 9-3 center dot 7]; p<0 center dot 0001) and pandemic decrease phase (2 center dot 3 [1 center dot 0-5 center dot 0]; p<0 center dot 0001). Compared with the prepandemic phase, in the pandemic decrease phase we observed an increased occurrence of thyroid tumours larger than 10 mm (2554 [69 center dot 0%] of 3704 vs 1515 [71 center dot 5%] of 2119; OR 1 center dot 1 [95% CI 1 center dot 0-1 center dot 3]; p=0 center dot 042), lymph node metastases (343 [9 center dot 3%] vs 264 [12 center dot 5%]; OR 1 center dot 4 [1 center dot 2-1 center dot 7]; p=0 center dot 0001), and tumours at high risk of structural disease recurrence (203 [5 center dot 7%] of 3584 vs 155 [7 center dot 7%] of 2006; OR 1 center dot 4 [1 center dot 1-1 center dot 7]; p=0 center dot 0039).Interpretation Our study suggests that the reduction in surgical activity for indeterminate thyroid nodules during the COVID-19 pandemic period could have led to an increased occurrence of aggressive thyroid tumours. However, other compelling hypotheses, including increased selection of patients with aggressive malignancies during this period, should be considered. We suggest that surgery for indeterminate thyroid nodules should no longer be postponed even in future instances of pandemic escalation.Funding None.Copyright (c) 2023 Published by Elsevier Ltd. All rights reserved
Recovery of dialysis patients with COVID-19 : health outcomes 3 months after diagnosis in ERACODA
Background. Coronavirus disease 2019 (COVID-19)-related short-term mortality is high in dialysis patients, but longer-term outcomes are largely unknown. We therefore assessed patient recovery in a large cohort of dialysis patients 3 months after their COVID-19 diagnosis. Methods. We analyzed data on dialysis patients diagnosed with COVID-19 from 1 February 2020 to 31 March 2021 from the European Renal Association COVID-19 Database (ERACODA). The outcomes studied were patient survival, residence and functional and mental health status (estimated by their treating physician) 3 months after COVID-19 diagnosis. Complete follow-up data were available for 854 surviving patients. Patient characteristics associated with recovery were analyzed using logistic regression. Results. In 2449 hemodialysis patients (mean ± SD age 67.5 ± 14.4 years, 62% male), survival probabilities at 3 months after COVID-19 diagnosis were 90% for nonhospitalized patients (n = 1087), 73% for patients admitted to the hospital but not to an intensive care unit (ICU) (n = 1165) and 40% for those admitted to an ICU (n = 197). Patient survival hardly decreased between 28 days and 3 months after COVID-19 diagnosis. At 3 months, 87% functioned at their pre-existent functional and 94% at their pre-existent mental level. Only few of the surviving patients were still admitted to the hospital (0.8-6.3%) or a nursing home (âŒ5%). A higher age and frailty score at presentation and ICU admission were associated with worse functional outcome. Conclusions. Mortality between 28 days and 3 months after COVID-19 diagnosis was low and the majority of patients who survived COVID-19 recovered to their pre-existent functional and mental health level at 3 months after diagnosis
Agroforesterie et services écosystémiques en zone tropicale
Respectueux de lâenvironnement et garantissant une sĂ©curitĂ© alimentaire soutenue par la diversification des productions et des revenus quâils procurent, les systĂšmes agroforestiers apparaissent comme un modĂšle prometteur dâagriculture durable dans les pays du Sud les plus vulnĂ©rables aux changements globaux. Cependant, ces systĂšmes agroforestiers ne peuvent ĂȘtre optimisĂ©s quâĂ condition de mieux comprendre et de mieux maĂźtriser les facteurs de leurs productions. Lâouvrage prĂ©sente un ensemble de connaissances rĂ©centes sur les mĂ©canismes biophysiques et socio-Ă©conomiques qui sous-tendent le fonctionnement et la dynamique des systĂšmes agroforestiers. Il concerne, dâune part les systĂšmes agroforestiers Ă base de cultures pĂ©rennes, telles que cacaoyers et cafĂ©iers, de rĂ©gions tropicales humides en AmĂ©rique du Sud, en Afrique de lâEst et du Centre, dâautre part les parcs arborĂ©s et arbustifs Ă base de cultures vivriĂšres, principalement de cĂ©rĂ©ales, de la rĂ©gion semi-aride subsaharienne dâAfrique de lâOuest. Il synthĂ©tise les derniĂšres avancĂ©es acquises grĂące Ă plusieurs projets associant le Cirad, lâIRD et leurs partenaires du Sud qui ont Ă©tĂ© conduits entre 2012 et 2016 dans ces rĂ©gions. Lâensemble de ces projets sâarticulent autour des dynamiques des systĂšmes agroforestiers et des compromis entre les services de production et les autres services socio-Ă©cosystĂ©miques que ces systĂšmes fournissent
26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15â20 July 2017
This work was produced as part of the activities of FAPESP Research,\ud
Disseminations and Innovation Center for Neuromathematics (grant\ud
2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud
FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud
supported by a CNPq fellowship (grant 306251/2014-0)
Projections stĂ©rĂ©ographiques ; TrigonomĂ©trie sphĂ©rique ; DĂ©termination du triangle des Ă©lĂ©ments et du triangle dĂ©rivĂ© de G. CesĂ ro : par la mĂ©thode des projections stĂ©rĂ©ographiques ; Ă l'usage de l'enseignement moyen (seconde et premiĂšre scientifique) et des candidats Ă l'universitĂ© et Ă l'Ăcole Royale Militaire (division polytechnique) /
EMI datalib: Joining the best of ARC and gLite data libraries
To manage data in the grid, with its jungle of protocols and enormous amount of data in different storage solutions, it is important to have a strong, versatile and reliable data management library. While there are several data management tools and libraries available, they all have different strengths and weaknesses, and it can be hard to decide which tool to use for which purpose. EMI is a collaboration between the European middleware providers aiming to take the best out of each middleware to create one consolidated, all-purpose grid middleware. When EMI started there were two main tools for managing data - gLite had lcg util and the GFAL library, ARC had the ARC data tools and libarcdata2. While different in design and purpose, they both have the same goal, to manage data in the grid. The design of the new EMI datalib was ready by the end of 2011, and a first prototype is now implemented and going through a thorough testing phase. This presentation will give the latest results of the consolidated library together with an overview of the design, test plan and roadmap of EMI datalib
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